Forecasting N2O emission and nitrogen loss from swine manure composting based on BP neural network

Nitrogen loss and greenhouse gas emission during compost will cause secondary pollution and waste nutrients. To address this issue, a predictive model was set up to obtain a clear knowledge of the N2O emission and nitrogen loss from swine manure composting. This paper collected 68 group data from 11 published papers about pig manure composting N2O emission and total nitrogen loss. Select 4 indexes were taken as predicted indexes include aeration rate, moisture content, C/N, and the amount of superphosphate to establish a BP neural network for forecasting the N2O emission and total nitrogen loss from composting. The analyses show that the mean error of N2O emission forecasting model is 1.17; the value of MAPE is 138.85%. As for nitrogen loss, the mean error is 24.72 and the mean absolute percentage error is 11.06%. Compare to the traditional linear regression, the BP neural network model has good accuracy on forecasting N2O emission and TN loss from manure composting. BP neural network has considerable application prospect in forecast nitrogen loss and greenhouse gas emission from composting

Forecasting N

Nitrogen loss and greenhouse gas emission during compost will cause secondary pollution and waste nutrients. To address this issue, a predictive model was set up to obtain a clear knowledge of the N2O emission and nitrogen loss from swine manure composting. This paper collected 68 group data from 11 published papers about pig manure composting N2O emission and total nitrogen loss. Select 4 indexes were taken as predicted indexes include aeration rate, moisture content, C/N, and the amount of superphosphate to establish a BP neural network for forecasting the N2O emission and total nitrogen loss from composting. The analyses show that the mean error of N2O emission forecasting model is 1.17; the value of MAPE is 138.85%. As for nitrogen loss, the mean error is 24.72 and the mean absolute percentage error is 11.06%. Compare to the traditional linear regression, the BP neural network model has good accuracy on forecasting N2O emission and TN loss from manure composting. BP neural network has considerable application prospect in forecast nitrogen loss and greenhouse gas emission from composting

COMMUNICATION NETWORK AND QOS EVALUATION FOR FORMATION CONTROL OF UNMANNED SURFACE VEHICLES

The main purpose of this article is to design efficient communication networks for the formation control of multiple distributed Unmanned Surface Vehicles (USVs). First, a hybrid communication network architecture, combining remote communication and wireless Ad hoc network technology is proposed. Second, an improved Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is adopted to prolong the life cycle of the communication network of the USV fleet. Subsequently, some QoS indicators of the USV communication network are evaluated by establishing wireless network channel model and Signal to Interference and Noise Ratio (SINR) model. In particular, the packet error ratio, average time delay and connectivity under different formation architecture are investigated. Finally, some discussions and future work on the QoS of the USV communication network are concluded

Characteristics and health impacts of bioaerosols in animal barns: A comprehensive study

Bioaerosols produced during animal production have potential adverse effects on the health of workers and animals. Our objective was to investigate characteristics, antibiotic-resistance genes (ARGs), and health risks of bioaerosols in various animal barns. Poultry and swine barns had high concentrations of airborne bacteria (11156 and 10917 CFU/m3, respectively). Acinetobacter, Clostridium sensu stricto, Corynebacterium, Pseudomonas, Psychrobacter, Streptococcus, and Staphylococcus were dominant pathogenic bacteria in animal barns, with Firmicutes being the most abundant bacterial phylum. Based on linear discriminant analysis effect size (LEfSe), there were more discriminative biomarkers in cattle barns than in poultry or swine barns, although the latter had the highest abundance of bacterial pathogens and high abundances of ARGs (including tetM, tetO, tetQ, tetW sul1, sul2, ermA, ermB) and intI1). Based on network analyses, there were higher co-occurrence patterns between bacteria and ARGs in bioaerosol from swine barns. Furthermore, in these barns, relative abundance of bacteria in bioaerosol samples was greatly affected by environmental factors, mainly temperature, relative humidity, and concentrations of CO2, NH3, and PM2.5. This study provided novel data regarding airborne bio-contaminants in animal enclosures and an impetus to improve management to reduce potential health impacts on humans and animals

CO₂ Absorption by Solvents Consisting of TMG Protic Ionic Liquids and Ethylene Glycol: The Influence of Hydrogen Bonds

Herein, the absorption of CO2 by the TMG-based (TMG: 1,1,3,3-tetramethylguanidine) ionic liquids (ILs) and the absorbents formed by TMG ILs and ethylene glycol (EG) is studied. The TMG-based ILs used are formed by TMG and 4-fluorophenol (4-F-PhOH) or carvacrol (Car), and their viscosities are low at 25 °C. The CO2 uptake capacities of [TMGH][4-F-PhO] and [TMGH][Car] are low (~0.09 mol CO2/mol IL) at 25 °C and 1.0 atm. However, the mixtures [TMGH][4-F-PhO]-EG and [TMGH][Car]-EG show much higher capacities (~1.0 mol CO2/mol IL) than those of parent ILs, which is unexpected because of the low CO2 capacity of EG (0.01 mol CO2/mol EG) in the same conditions. NMR spectra and theoretical calculations are used to determine the reason for these unexpected absorption behaviors. The spectra and theoretical results show that the strong hydrogen bonds between the [TMGH]+ cation and the phenolate anions make the used TMG-based ILs unreactive to CO2, resulting in the low CO2 capacity. In the Ils-EG mixtures, the hydrogen bonds formed between EG and phenolate anions can weaken the [TMGH]+–anion hydrogen bond strength, so ILs-EG mixtures can react with CO2 and present high CO2 capacities

Investigating the Sustainability Performance of PPP-Type Infrastructure Projects: A Case of China

In China, the demand for public infrastructure projects is high due to the acceleration of urbanization and the rapid growth of the economy in recent years. Infrastructures are mainly large scale, so local governments have difficulty in independently completing financing work. In this context, public sectors often seek cooperation from private sectors, in which public⁻private partnership (PPP) is increasingly common. Although numerous studies have concentrated on sustainable development, the unsustainability performances of infrastructures are often reported on various media. Furthermore, studies on the sustainability performances of PPP-type infrastructure (PTI) projects are few from the perspective of private sectors’ behaviors. In this study, we adopted the modified theory of planned behavior and the structure equation model and conducted a questionnaire survey with 258 respondents for analyzing the sustainable behaviors of private sectors. Results indicated that behavioral attitude, perceived behavioral control, and subjective norm interact significantly. They have direct positive effects on behavioral intention and then indirectly influence actual behavior through this intention. Actual sustainable behaviors of private sectors have significantly positive effects on the sustainable development of cities. We offer theoretical and managerial implications for public and private sectors on the basis of the findings to ensure and promote the sustainability performances of PTI projects

Overcoming the permeability-selectivity challenge in water purification using two-dimensional cobalt-functionalized vermiculite membrane

Abstract Clean water and sanitation are major global challenges highlighted by the UN Sustainable Development Goals. Water treatment using energy-efficient membrane technologies is one of the most promising solutions. Despite decades of research, the membrane permeability-selectivity trade-off remains the major challenge for synthetic membranes. To overcome this challenge, here we develop a two-dimensional cobalt-functionalized vermiculite membrane (Co@VMT), which innovatively combines the properties of membrane filtration and nanoconfinement catalysis. The Co@VMT membrane demonstrates a high water permeance of 122.4 L·m−2·h−1·bar−1, which is two orders of magnitude higher than that of the VMT membrane (1.1 L·m−2·h−1·bar−1). Moreover, the Co@VMT membrane is applied as a nanofluidic advanced oxidation process platform to activate peroxymonosulfate (PMS) for degradation of several organic pollutants (dyes, pharmaceuticals, and phenols) and shows excellent degradation performance (~100%) and stability (for over 107 h) even in real-world water matrices. Importantly, safe and non-toxic effluent water quality is ensured by the Co@VMT membrane/PMS system without brine, which is totally different from the molecular sieving-based VMT membrane with the concentrated pollutants remaining in the brine. This work can serve as a generic design blueprint for the development of diverse nanofluidic catalytic membranes to overcome the persistent membrane permeability-selectivity issue in water purification

Technical Challenges of Safety Emergency Drawdown for High Dam and Large Reservoir Project

With the development of a 300 m high dam and large reservoir construction, the emergency drawdown capacity of cascade reservoirs, especially high dams, has become a hot issue of concern to all sectors of society while giving play to huge comprehensive benefits. Based on a thorough investigation of the current situation of drawdown facilities for high dams and large reservoirs with a height of 200 m or more in the world, this paper finds that drawdown facilities currently face difficulties such as insufficient drawdown capacity, poor safety and stability of high head structures, extremely high lift hoisting equipment, and high difficulty in high head water seal technology. It is pointed out that the key technologies that need to be urgently addressed for a deep drawdown of high dams and large reservoirs are the pressure-bearing capacity of gates and the capacity limit of hoists. As a result, the elevation of the bottom tunnel of the drawdown building cannot be arranged and the orifice is limited, and the drawdown depth and discharge capacity are limited